Anti-Inflammatory Activity of Lauraceae Plant Species and Prediction Models Based on Their Metabolomics Profiling Data |
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Authors: | Bianca Gonçalves Vasconcelos de Alcântara Albert Katchborian Neto Daniela Aparecida Garcia Rosana Casoti Tiago Branquinho Oliveira Ana Claudia Chagas de Paula Ladvocat RuAngelie Edrada-Ebel Marisi Gomes Soares Danielle Ferreira Dias Daniela Aparecida Chagas de Paula |
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Institution: | 1. Laboratory of Phytochemistry, Medicinal Chemistry and Metabolomics, Chemistry Institute, Federal University of Alfenas, 37130-001 Alfenas, MG, Brazil;2. Antibiotics Department, Federal University of Pernambuco., 50670-901 Recife, PE, Brazil;3. Department of Pharmacy, Federal University of Sergipe, 491000-000 São Cristóvão, SE, Brazil;4. Department of Pharmaceutical Sciences, Federal University of Juiz de Fora, 36036-900 Juiz de Fora, MG, Brazil;5. Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, G4 0RE Glasgow, Scotland |
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Abstract: | The Lauraceae is a botanical family known for its anti-inflammatory potential. However, several species have not yet been studied. Thus, this work aimed to screen the anti-inflammatory activity of this plant family and to build statistical prediction models. The methodology was based on the statistical analysis of high-resolution liquid chromatography coupled with mass spectrometry data and the ex vivo anti-inflammatory activity of plant extracts. The ex vivo results demonstrated significant anti-inflammatory activity for several of these plants for the first time. The sample data were applied to build anti-inflammatory activity prediction models, including the partial least square acquired, artificial neural network, and stochastic gradient descent, which showed adequate fitting and predictive performance. Key anti-inflammatory markers, such as aporphine and benzylisoquinoline alkaloids were annotated with confidence level 2. Additionally, the validated prediction models proved to be useful for predicting active extracts using metabolomics data and studying their most bioactive metabolites. |
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Keywords: | Lauraceae multivariate statistical analyses metabolomics anti-inflammatory mass spectrometry |
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